The authors are concerned with a bidding problem. There are two companies (P1 and P2) bidding for a highway construction project. In order to be more competitive, P1 considers buying a new gravel pit near the construction site. The basic cost of the pit is known to both companies. However, there is also an additional, hidden, cost (C) known only to P1. P2 is uncertain whether the hidden cost is C = 0 or C = x. P1 plans to bid for the job, but has to decide whether to buy the gravel pit. P2, not having a complete knowledge about C, thus not knowing the strategy choice of P1, has to decide if to bid for the job. In effect we have two payoff matrices, one for the additional cost C = 0, and the other one for C = x. If the probability of P2 bidding for the project can be estimated by propagating intelligence information through a Bayesian Belief Network, the best strategy for P1 can be readily determined. Otherwise, the solution calls for changing this game of incomplete information (players may or may not know some information about the other players, e.g., their "type," their strategies, payoffs) into a game of imperfect information (players are simply unaware of the actions chosen by other players). This is achieved by introducing an additional "Nature" node which for this problem determines with some probability "p" the additional cost C= 0 (thus, C = x with probability 1-p). The solution of this game turns out to depend on the probability "p". For some values of p the game is solved with pure strategies, whereas for other values the game is in equilibrium when the players randomly mix their strategies.

Traffic–induced vibrations may constitute a considerable load to buildings. In this paper, vibrations transmitted through the ground caused by wheeled vehicles are considered. This phenomenon may cause cracking of plaster, cracks in load-bearing elements or even, in extreme cases, collapse of the whole structure. Measurements of vibrations of real structures are costly and laborious. Therefore, the aim of the present paper is to propose a method of using Bayesian networks combined with implementation of geoscience for assessment of impact of traffic–induced vibrations on residential buildings. Firstly, the experimental tests were performed on different buildings using specialized equipment taking into account five factors: Distance from the building to the edge of the road, condition of road surface, condition of the building, the absorption of soil and the type of vehicle. Then, probabilistic analyses applying Bayesian networks were conducted and two methods of assessing the information value (EVSI method and entropy method) were compared. Finally, the developed diagnostic–decision support model was tested, so as to verify the most important parameter, affecting the possibility of structural vibrations to occur. The results of the study clearly showed that the use of Bayesian networks was a very effective approach to assess the impact of traffic-induced vibrations. The developed algorithm could be successfully applied both to existing and planned buildings, for which the source of vibration is already present or may appear in the future.

The civil-engineering projects in urban conditions are usually quite complex. They also are subject to various types of risk factors. The cumulative high risk of such projects negatively affects the planning and execution of the works, causing delays, to the extent of making it sometimes impossible to complete a given construction phase. The aim of the article is to discuss the main factors causing disruptions in the performance of engineering works in the urban environment, and to demonstrate a methodology that could be used to determine the expected increase in cost and/or construction duration caused by the risk factors. The created model combines the risk factors, expected risk consequences (such as increase in cost), and the effects of mitigating activities. The model uses Bayesian Belief Nets as the computing engine.

An important goal, implemented by EU countries under the Europe 2020 strategy, is sustainable development, which includes supporting economy that effectively uses natural and environmentally friendly resources. Solutions in this area are also promoted in tender proceedings in the area of public procurement. The LCA (Life Cycle Assessment) and LCC (Life Cycle Cost) analysis are indicated as the basis for decision-making by awarding entities. In the article, the authors present on the selected example the benefits of using LCA and LCC. Based on the documents analysis for 350 selected public procurement procedures conducted in Poland in 2017, the authors examine types, average weights and frequency of application of non-price criteria for the selection of the best offer in practice. Based on the results of the research, are formulated conclusions.

The authors are seeking new methods for improving the efficiency of the investments associated with the maintenance and operation of existing civil engineering structures. It is demonstrated how the knowledge about the elements of construction and operation phases and their relationships, combined with monitoring data can be used for more effective management of the risks associated with civil engineering projects. The methodology chosen for estimating the probabilities of risk events is known as Bayesian Belief Networks (BBNs). To better illustrate how the proposed approach works the authors use the example of multi-family residential building located in Gdansk made in the wood-frame technology.